论文标题

关于量化论证中的SCC征收性

On SCC-recursiveness in Quantitative Argumentation

论文作者

Wang, Zongshun, Shen, Yuping

论文摘要

抽象论证是一种基于各种语义的论证的推理模型。 SCC恢复性是语义的复杂属性,它为通过沿牢固连接的组件(SCC)的分解来表征语义的一般架构。尽管该属性在各种定性框架中进行了广泛的探索,但在定量论证中相对忽略了它。为了填补这一空白,我们证明了该属性非常适合模糊扩展语义,这是对模糊论证框架(FAF)中经典语义的定量概括。我们根据SCC沿其SCC的递归分解来量身定制SCC恢复模式,以实现模糊扩展语义的表征。我们的贡献是双重的。从理论上讲,我们表明,SCC恢复性提供了一种表征模糊扩展语义的替代方法,提供了深刻的理解并更好地了解这些语义。实际上,我们的模式为计算模糊扩展语义提供了一种声音和完整的算法,该语义自然会在处理大量SCC时减少计算工作。

Abstract argumentation is a reasoning model for evaluating arguments based on various semantics. SCC-recursiveness is a sophisticated property of semantics that provides a general schema for characterizing semantics through the decomposition along strongly connected components (SCCs). While this property has been extensively explored in various qualitative frameworks, it has been relatively neglected in quantitative argumentation. To fill this gap, we demonstrate that this property is well-suited to fuzzy extension semantics, which is a quantitative generalization of classical semantics in fuzzy argumentation frameworks (FAF). We tailor the SCC-recursive schema to enable the characterization of fuzzy extension semantics through the recursive decomposition of an FAF along its SCCs. Our contributions are twofold. Theoretically, we show that SCC-recursiveness provides an alternative approach to characterize fuzzy extension semantics, offering a deep understanding and better insight into these semantics. Practically, our schema provides a sound and complete algorithm for computing fuzzy extension semantics, which naturally reduces computational efforts when dealing with a large number of SCCs.

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